Structured neural networks for multi-font Chinese character recognition using a newly developed digital neural network chip with adaptive segmentation of quantizer neuron architecture (ASQA)
K. Kondo, Taro Imagawa, S. Maruno · 2002
This paper describes structured networks that use a digital network chip with having adaptive segmentation of quantizer neuron architecture (ASQA) and presents results of applying the ASQA chip to the large scale problem of multi-font Chinese character recognition. The ASQA chip can simulate neural networks using ASQA model which can provide a proliferation of neurons based on input data for learning and can generate appropriate network structure with extremely fast processing speed. Moreover, this chip can simulate not only a single network but also sets of several structured networks; consequently, the chip can handle large scale problems. By applying the chip to multi-font Chinese character recognition, average accuracy of the open test increased to 97% and a recognition speed of 6 msec/character was achieved.